Work

MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning

Year: 2022

Type: article

Abstract: Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning methods, we present pathogenicity prediction models, MetaRNN and MetaRNN-indel, to help identify and prioritize rare nonsynonymous single nucleotide variants (nsSNVs) and non-frameshift insertion/deletions (nfINDELs). We use independent test sets to demonstrate that these new models outperform state-of-the-art competitors and achieve a more interpretable score distribution. Importantly, prediction scores from both models are comparable, enabling easy adoption of integrated genotype-phenotype association analysis methods. All pre-computed nsSNV scores are available at http://www.liulab.science/MetaRNN . The stand-alone program is also available at https://github.com/Chang-Li2019/MetaRNN .

Source: Genome Medicine

DOI: 10.1186/s13073-022-01120-z

Authors: Chang Li, Degui Zhi, Kai Wang, Xiaoming Liu

Institutions: University of South Florida, The University of Texas Health Science Center at Houston, Children's Hospital of Philadelphia, University of Pennsylvania

Language: English

Topic: Genomics and Rare Diseases

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $4,480